Online ISSN: 2515-8260

Keywords : machine learning models

A Survey of different machine learning models for software defect testing

Arpitha Kotte; Dr.Ahmad Abdul Moiz Qyser

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 3256-3268

As the size of the defects increases, it becomes difficult to predict the different types of software defects with high true positive rate. The main objective of the machine learning models for software defect-based testing application is to improve the defect prediction rate with less error rate. Evaluating the software metrics and defect prediction are the two key quality features that determine the success of a software product. Most of the conventional meta-heuristic based software defect testing models are independent of dynamic parameters estimation. Also, these conventional models are used to predict the defect in the homogeneous software testing systems with limited number of feature space. In this paper, different types of software defect prediction systems and its models are discussed along with the limitations on various metrics.


Kanaga Suba Raja. S; Usha Kiruthika; V. Balaji; Anjana Sriram

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2946-2951

Covid-19 prediction models are the most welcome and need of the hour in this current pandemic situation for paving way for decision making by Authorities. India due to its protective characteristics against COVID-19 is supposed to have the least death rate compared to nations having similar number of Covid cases. But the recent increase in the death toll has been a matter of deep concern. The SEIR models which were practiced earlier could not predict death rates accurately due to certain limitations in the procedure. Hence this paper is presented with a model which can efficiently predict the deaths due to covid-19 in every state in India. The aim of this paper is to present a model which predicts the number of fatalities caused by corona with maximum efficacy.